At the Humber River Hospital in northwest Toronto, artificial intelligence is helping to identify peak service times and other bottlenecks in the emergency room.

There is no doubt that artificial intelligence is changing the medical landscape. This can be found in some emergency rooms, and soon we will see it in all aspects of medical care. In fact, it could be said that both radiology and dermatology between areas of medicine will be altered by machines with the capacity to learn.

In this way, experts in computer science and medicine have expressed that artificial intelligence in health care has great potential, but also raises serious questions about bias, responsibility, and safety.

Possibly we are just seeing the tip of the iceberg at this time. That being the case, and there are those who believe that an AI will improve medical care. Therefore, scientists are already using AI to develop medical devices.

Change in medical panorama

It should be noted that, at the University of Alberta, researchers are testing an experimental bionic arm that can “learn” and anticipate the movements of an amputee. Similarly, last year the Food and Drug Administration of the United States approved a tool that can examine your retina and automatically detect signs of diabetic blindness.

That being the case, the IA is expected to soon affect all aspects of medical care. The ability to quickly disseminate large amounts of information will have a great impact on medical diagnoses, and medical experts believe that pathology, dermatology, and radiology will probably be the first to see these changes.

An important fact to note is that at Humber River Hospital, in northwestern Toronto, the AI is accelerating perhaps the most frustrating part of a patient’s experience: the emergency room.

Likewise, at the hospital’s control center, powerful computers now accurately predict how many patients will arrive at the emergency department two days in advance. The software processes real-time data from the entire hospital (admissions, waiting times, transfers and discharges) and analyzes them, and goes back more than a year of information. From that, you can find patterns and point out bottlenecks in the system. Solve those bottlenecks and you could end up with more satisfied patients, as well as achieve a better final result.